Fake e-shop detection

Abstract

A method, apparatus, and system for website filtering includes a processor and a memory having stored therein at least programs or instructions executable by the processor to cause the system to load HTML content of a requested website, extract website indicators from the loaded HTML content, perform feature engineering on the extracted website indicators, filter the website by applying a machine learning model trained to analyze the engineered website indicators to predict whether a resource of the website is associated with a fake e-shop, and if it is determined that a resource of the requested website is associated with a fake e-shop, generate and transmit a website filter determination that the resource of the website is associated with a fake e-shop.

What is claimed is: 
     
         1 . A website filtering system comprising:
 a hardware processor; and a memory accessible by the processor, the memory having stored therein at least one of programs or instructions executable by the at least one processor to cause the filtering system to perform operations comprising:   receiving a request to access a resource associated with a website;   loading HTML content of the requested website;   extracting website indicators from the loaded HTML content;   performing feature engineering on the extracted website indicators;   filtering the website by applying a machine learning model trained to analyze the website indicators to predict whether a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, generating and transmitting a website filter determination that the resource of the website is associated with a fake e-shop.   
     
     
         2 . The system according to  claim 1 , wherein the filtering system further performs:
 filtering the website by comparing the, extracted website indicators to a blocklist of website indicators identified as being associated with at least one fake e-shop to predict if a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, updating the blocklist to include an identification of at least one of the extracted website indicators or the requested website.   
     
     
         3 . The system according to  claim 2 , wherein the extracted website indicators are compared to a blocklist of website indicators to predict if a resource of the website is associated with a fake e-shop according to determined blocklist rules. 
     
     
         4 . The system according to  claim 3 , wherein the blocklist rules are determined from patterns recognized in at least a portion of text of at least one website in the blocklist. 
     
     
         5 . The system according to  claim 4 , wherein the patterns include related words in a website or a context of words in a website. 
     
     
         6 . The system according to  claim 2 , wherein if it is determined that a resource of the website is not associated with a fake e-shop, the filtering system further performs:
 filtering the website by comparing at least one visual feature of the resource associated with the website with at least one respective visual feature of a known legitimate website to identify similarities and/or differences to determine if the resource associated with the website is associated with a fake e-shop.   
     
     
         7 . The system according to  claim 6 , further comprising a threshold wherein if an amount of the similarities are below the threshold or an amount of the differences are above the threshold, the resource of the website is determined to be associated with a fake e-shop. 
     
     
         8 . The system according to  claim 6 , further comprising a machine learning model trained to identify similarities and/or differences between the at least one visual feature of a resource of the requested website and the at least one respective visual feature of the known legitimate website to determine if a resource of the website is associated with a fake e-shop. 
     
     
         9 . The system according to  claim 1 , wherein the website indicators comprise patterns identified in previously known fake e-shop websites. 
     
     
         10 . The system according to  claim 9 , wherein the website indicators comprise at least one of external indicators, HTML indicators, or large learning model (LLM)-determined indicators. 
     
     
         11 . A website filtering method comprising:
 receiving a request to access a resource associated with a website;   loading HTML content of the requested website;   extracting website indicators from the loaded HTML content;   performing feature engineering on the extracted website indicators;   filtering the website by applying a machine learning model trained to analyze the website indicators to predict whether a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, generating and transmitting a website filter determination that the resource of the website is associated with a fake e-shop.   
     
     
         12 . The method according to  claim 11 , further comprising:
 filtering the website by comparing the extracted website indicators to a blocklist of website indicators identified as being associated with at least one fake e-shop to predict if a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, updating the blocklist to include an identification of at least one of the extracted website indicators or the requested website.   
     
     
         13 . The method according to  claim 12 , comprising:
 comparing the website indicators to a blocklist of website indicators to predict if a resource of the website is associated with a fake e-shop according to predetermined blocklist rules.   
     
     
         14 . The method according to  claim 12 , further comprising:
 if it is determined that a resource of the website is not associated with a fake e-shop, filtering the website by comparing at least one visual feature of the resource associated with the website with at least one respective visual feature of a known legitimate website to identify similarities and/or differences to determine if the resource associated with the website is associated with a fake e-shop.   
     
     
         15 . The method according to  claim 14 , further comprising identifying similarities and/or differences between the at least one visual feature of a resource of the requested website and the at least one respective visual feature of the known legitimate website using a machine learning model trained to determine if a resource of the website is associated with a fake e-shop. 
     
     
         16 . The method according to  claim 11 , wherein the website indicators comprise patterns identified in previously known fake e-shop websites. 
     
     
         17 . The method according to  claim 16 , wherein the website indicators comprise at least one of external indicators, HTML indicators, or large learning model (LLM)-determined indicators. 
     
     
         18 . A non-transitory computer readable medium, which when executed by a processor and a memory, performs a website filtering method comprising:
 receiving a request to access a resource associated with a website;   loading HTML content of the requested website;   extracting website indicators from the loaded HTML content;   performing feature engineering on the extracted website indicators;   filtering the website by applying a machine learning model trained to analyze the website indicators to predict whether a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, generating and transmitting a website filter determination that the resource of the website is associated with a fake e-shop.   
     
     
         19 . The non-transitory computer readable medium according to  claim 18 , wherein the method further comprises:
 filtering the website by comparing the extracted website indicators to a blocklist of website indicators identified as being associated with at least one fake e-shop to predict if a resource of the website is associated with a fake e-shop; and   if it is determined that a resource of the requested website is associated with a fake e-shop, updating the blocklist to include an identification of at least one of the extracted website indicators or the requested website.   
     
     
         20 . The non-transitory computer readable medium according to  claim 19 , comprising:
 comparing the extracted website indicators to a blocklist of website indicators to predict if a resource of the website is associated with a fake e-shop according to predetermined blocklist rules.   
     
     
         21 . The non-transitory computer readable medium according to  claim 20 , wherein the blocklist rules are determined from patterns recognized in at least a portion of text of at least one website in the blocklist. 
     
     
         22 . The non-transitory computer readable medium according to  claim 21 , wherein the patterns include related words in a website or a context of words in a website. 
     
     
         23 . The non-transitory computer readable medium according to  claim 19 , further comprising:
 if it is determined that a resource of the website is not associated with a fake e-shop, filtering the website by comparing at least one visual feature of the resource associated with the website with at least one respective visual feature of a known legitimate website to identify similarities and/or differences to determine if the resource associated with the website is associated with a fake e-shop.   
     
     
         24 . The non-transitory computer readable medium according to  claim 23 , further comprising identifying similarities and/or differences between the at least one visual feature of a resource of the requested website and the at least one respective visual feature of the known legitimate website using a machine learning model trained to determine if a resource of the website is associated with a fake e-shop. 
     
     
         25 . The non-transitory computer readable medium according to  claim 18 , wherein the website indicators comprise patterns identified in previously known fake e-shop websites. 
     
     
         26 . The non-transitory computer readable medium according to  claim 25 , wherein the website indicators comprise at least one of external indicators, HTML indicators, or large learning model (LLM)-determined indicators.